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## Summary De-flake `test_arun_with_retry_timeout_observer_emits_progress_on_heartbeat` — the test was hitting a CI-runner-load-sensitive race where the idle-timeout watchdog could fire before the task body's first await ran. ## Root cause `_TimedAttemptScope.__init__` sets `_last_progress = time.monotonic()` immediately, but the watchdog itself doesn't start polling until *after* `wrap_config` and task scheduling. Under heavy CI load that gap can grow large enough that: ``` T₀ scope.__init__() → _last_progress = T₀ … some scheduling slack … Tₙ watchdog runs, computes remaining = T₀ + 0.2 − Tₙ ≤ 0 → TimeoutError fires ``` The error reports `elapsed: 0.000s` because `elapsed` is measured from the post-scheduling `start` (≈Tₙ), not from `_last_progress` (T₀). The previous test set `idle_timeout=0.2s`, which left almost no headroom for that scheduling slack. ## Fix (test-side only — no production change) - **Heartbeat at task-body entry**: `runtime.heartbeat()` is now called before the first `await asyncio.sleep(...)`, which resets `_last_progress` to "now" the moment the task body actually starts running. This eliminates the scope-init-to-first-await gap as a flake source. - **Idle timeout 0.2s → 1.0s**: gives ~5× headroom over the ~400ms task duration, so scheduling pressure stays comfortably within budget. ## Why test-side instead of fixing the production race The proper production fix would be to set `_last_progress` at watchdog-entry time rather than at scope-init time. That's a behaviour change in the retry/timeout machinery and out of scope for a flaky-test fix. The two test-side defenses make this particular test stable without touching production semantics; the underlying race in `_TimedAttemptScope` is worth a separate follow-up. ## Test plan - 10/10 repeated local runs pass: ``` uv run pytest tests/test_retry.py::test_arun_with_retry_timeout_observer_emits_progress_on_heartbeat --count=10 ``` - All assertions still meaningful: still verifies start/finish events, at least one progress event, rate-limited progress count (≤ total events), and per-event metadata (task_name, attempt, idle_timeout_secs, progress_at). Co-authored-by: Cursor <cursoragent@cursor.com>
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.